similar to: Question regarding significance of a covariate in a coxme survival

Hi, I am running a Cox Mixed Effects Hazard model using the library coxme. I am trying to model time to onset (age_sym1) of thought problems (e.g. hearing voices) (sym1). As I have siblings in my dataset, I have decided to account for this by including a random effect for family (famid). My covariate of interest is Mother's diagnosis where a 0 is bipolar, 1 is control, and 2 is major

Dear R users: I'm new to R and am trying to fit a mixed model Cox regression model with coxme function. I have one two-level factor (treat) and one covariate (covar) and 32 different groups (centers). I'd like to fit a random coefficients model, with treat and covar as fixed factors and a random intercept, random treat effect and random covar slope per center. I haver a couple of

Dear R community I currently try to get post hoc multiple comparisons with the package multcomp from a cox mixed-effects model, where the survival is explained by two variables (cover with levels: nocover and cover; treatment with levels: tx, uv, meta), whose interaction is significant. I read Hothorn, T. 2011: Additional multcomp Examples and there is an example involving a two-way ANOVA with

Hi there, I have a question on fitting data by coxme. In particular I want to fit a random intercept and random slope cox model. Using the rats dataset as an example, I generated another covariate x2 and want to specify a random slope for x2. Here is my code: x2=matrix(rep(runif(50), 3), 50, 3) x2=as.vector(t(x2)) rats2=cbind(rats, x2) But when I used the coxme function as follows, it gave

Dear R experts; I am trying to extract the p values from a coxme object (package coxme). I can see the value in the model output, but I wanted to have the result with a higher number of decimal places. I have searched the mailing list and followed equivalent suggestions for nlme/lme objects, but I wasn't successful. Thanks; Catarina [[alternative HTML version deleted]]

Dear all, I am using "coxme" function in Kinship library to fit random treatment effect nested within centre. I got 3 treatments (0,1,2) and 3 centres. I used following commands, but got an error. > ugroup=paste(rep(1:3,each=3),rep(0:2,3),sep='/') > mat1=bdsmatrix(rep(c(1,1,1,1,1,1,1,1,1),3),blocksize=rep(3,3),dimnames=list(ugroup,ugroup)) >

Dear List, In documents (Therneau, 2003 : On mixed-effect cox models, ...), as far as I came to know, coxme penalize the partial likelihood (Ripatti, Palmgren, 2000) where as frailtyPenal (in frailtypack package) uses the penalized the full likelihood approach (Rondeau et al, 2003). How, then, coxme and coxph(...frailty(id, dist='gauss')) differs? Just the coding algorithm, or in

Dear all, I have encountered the following problem where coxme seems to allow model with only random effect in R 2.11.1 but not in R 2.13.0. Following is the error message using rat example data. Any comment on this is appreciated. In R2.13 > library(coxme) > rat1 <- coxme(Surv(time, status) ~ rx + (1|litter), rats) > rat0 <- coxme(Surv(time, status) ~ (1|litter), rats)

Hi I'm encountering some problems with coxme My data: I'm looking at the survival of animals in an experiment with 3 treatments, which came from 4 different populations, two of which were infected with a parasite and two of which were not. I'm interested if infected animals differe from uninfected ones across treatments. Factor 1: treatment (3 levels) Factor 2: infection state

Hello, I am running R 2.6.1 on windows xp I am trying to fit a cox proportional hazard model with a shared Gaussian frailty term using coxme My model is specified as: nofit1<-coxme(Surv(Age,cen1new)~ Sex+bo2+bo3,random=~1|isl,data=mydat) With x1-x3 being dummy variables, and isl being the community level variable with 4 levels. Does anyone know if there is a way to get the standard error

Dear R users, I'm using the function coxme of the package coxme in order to build Cox models with complex random effects. Unfortunately, I sometimes get surprising estimations of the variances of the random effects. I ran models with different fixed covariates but always with the same 3 random effects defined by the argument varlist=coxmeMlist(list(mat1,mat2,mat3), rescale = F, pdcheck = F,

Dear all, This probably stems from my lack of understanding of the model, but I do not understand the variance of the random effect reported in coxme. Consider the following toy example: #------------------------------- BEGINNING OF CODE ------------------------------------------------ library(survival) library(coxme) #--- Generate toy data: d <- data.frame(id = c(1:100), #

Hi I would really appreciate some help with my code for coxme... My data set I'm interested in survival of animals after an experiment with 4 treatments, which was performed on males and females. I also have two random factors: Response variable: survival (death) Factor 1: treatment (4 levels) Factor 2: sex (male / female) Random effects 1: person nested within day (2 people did

Hi all, I am using the coxme function to fit random effects survival models. The base survival models show a typical "J" shaped curve where survival drops sharply then plateaus over time interval. I want to include a time covariate which could account for this non-linear decrease in survival over time. However, I have not found a clear way to do this in the coxme models. Any

I am a relative newbie to survival analysis and R in general, but would like to use the coxme package to analyse some data I currently have. The data is relative to survival times of drosophila melanogaster populations to infection with pathogens, and has the variables: Time, Status, Treatment (4 treatments + 2 controls) Population Replicate ?and I'm currently using the following call

Dear list, I have been trying to use coxme in R 2.3.1. When I use coxme in the following data sim.fr1, i get "Warning messages: using 'as.environment(NULL)' is deprecated" Why does it occur? How can I hide such warning message, especially when coxme is under a loop? Mohammad Ehsanul Karim (Institute of Statistical Research and Training, University of Dhaka) >

Hello, Why the correlation between the random effects is negative? library(coxme) rats1 <- coxme(Surv(time, status) ~ (1|litter), rats) random.effects(rats1)[[1]] #one value for each of the 50 litters print(rats1) rats2 <- lmekin(time ~ (1|litter), rats) fixed.effects(rats2) random.effects(rats2)[[1]] #one value for each of the 50 litters print(rats2)

Hello, I was hoping someone could answer a question for me that may either be statistical or script related. I don't come from a statistics background, so I am not positive if I am using the correct nomenclature or even the correct procedure. Is it possible to model "random coefficients" in a mixed effects cox-regression using coxme from the Kinship package? For example, using

Hi, I am new to R and AIC scores but what I get from coxme seems wrong. The AIC score increases as p-values decrease. Since lower AIC scores mean better models and lower p-values mean stronger effects or differences then shouldn't they change in the same direction? I found this happens with the data set rats as well as my own data. Below is the output for two models constructed with the rats

Hello,I would like to know if there is residue analysis after the Cox model and make the adjustment for my random cluster? [[alternative HTML version deleted]]